Shape Estimation and Gesture Recognition for Origami-Based Deformable Surfaces

An origami tessellation display is an origami-based input/output device that has a flexible and unique deformable surface. This study achieves shape estimation and user gesture recognition for an origami-based surface without the use of external devices. Six tri-axis accelerometers are employed and embedded inside the surface, and shape estimation and gesture recognition models are constructed by means of neural networks and a random forest. Despite the small number of sensors and simple machine learning approaches, the models demonstrate sufficient estimation and recognition ability. In this paper, a framework in which shape estimation and gesture recognition are performed in real time is also presented.